One of the problems facing Big Data analytics is the making sense of large volumes of seemingly disconnected data. For example, a social media platform has hundreds of millions of users, who generate petabytes of data per day. Search engines also process a comparable volume of data per day. Managing or consuming such vast amounts of data on an ongoing basis in a meaningful way is a very complex problem.
Another one of the problems facing Big Data analytics is making sense of such large volumes of data quickly. Given enough time and computing power, any volume of data can be analyzed to obtain the desired answers from the volume of data. But, given that the volume of data is not static, and given that unlimited time and computing resources are usually unavailable in a practical environment, the speed at which large volumes of data can be analyzed is a critical factor in harnessing the value of the data before the data becomes obsolete or an opportunity to use the data is lost.